Policies for Reducing Emissions from Deforestation and Forest Degradation, known as REDD, and enhancing forest carbon stocks, known as REDD+, could provide a way for tackling global warming and climate change. In this regard several proposals were designed, yet their implementation poses significant methodological problems. One of those problems can be the interactions between the direct and indirect causes (drivers) of deforestation. Deforestation is a transformation of forestland for various land uses. This chapter therefore analyses trends in world deforestation in relation to different geographical regions and its drivers. A cross-sectional econometric model, recursive in nature, is estimated in two stages for addressing the interaction between the causes. Firstly, the direct causes of deforestation are regressed on indirect causes, by Seemingly Unrelated Regression (SUR) estimation to account for the correlations between the direct causes. Secondly, the SUR estimates of the direct causes are used for the regression of deforestation equation. The statistical evidences show prevalence of omitted variables for the indirect causes, as well as correlations between the direct causes. The SUR estimates are therefore efficient than OLS estimates. The results are discussed, in relation to Asian, African and Latin American regions, to provide guidance for designing effective REDD+ policies.
|Title of host publication||Deforestation|
|Subtitle of host publication||Conservation policies, economic implications and environmental impact|
|Editors||Carlos Narciso Bouza Herrera|
|Place of Publication||United States|
|Publisher||Nova Science Publishers|
|Number of pages||20|
|Publication status||Published - 2013|
Culas, R. (2013). The trends and the drivers of deforestation: A cross-country seemingly unrelated regression analysis for the REDD+ policies. In C. N. B. Herrera (Ed.), Deforestation: Conservation policies, economic implications and environmental impact (pp. 81-100). Nova Science Publishers.